MapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach
| Autor: | Yasser Abduallah, Miguel Cervantes-Cervantes, Turki Turki, Zongxuan Du, Jason T. L. Wang, Kevin Byron |
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| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
FOS: Computer and information sciences Time Factors Article Subject Computer science Molecular Networks (q-bio.MN) Gene regulatory network Information Theory lcsh:Medicine Cloud computing Saccharomyces cerevisiae General Biochemistry Genetics and Molecular Biology Regulatory molecules Computational Engineering Finance and Science (cs.CE) 03 medical and health sciences 0302 clinical medicine Quantitative Biology - Molecular Networks Quantitative Biology - Genomics Gene Regulatory Networks Computer Science - Computational Engineering Finance and Science Oligonucleotide Array Sequence Analysis Regulation of gene expression Genomics (q-bio.GN) Data processing General Immunology and Microbiology Series (mathematics) Microarray analysis techniques business.industry lcsh:R Experimental data General Medicine 030104 developmental biology 030220 oncology & carcinogenesis FOS: Biological sciences ComputingMethodologies_GENERAL business Algorithm Algorithms Research Article |
| Zdroj: | BioMed Research International BioMed Research International, Vol 2017 (2017) |
| DOI: | 10.48550/arxiv.1704.06548 |
| Popis: | Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their regulatory molecules, usually transcription factors, and a descriptive model of such connections, are known as gene regulatory networks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions. To date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes increases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome to test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data processing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from cellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here we propose new MapReduce algorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an information-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce program is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool. Comment: 19 pages, 5 figures |
| Databáze: | OpenAIRE |
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